In the forward prediction pass, we use the input agent histories, \(\textbf {x}\)  and \(\textbf {b}\) , along with the map context \(\mathcal {M}\) , to predict the target agent future trajectories \(\textbf {y}^{pk}\) .
We use the standard winner-takes-all multimodal prediction loss [1]} as the forward loss, which has a mode classification term and a trajectory regression term.
The trajectory regression term is computed on the prediction trajectory that is closest to the ground-truth measured by the final displacement error (FDE).
